{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gensim Tutorial on Online Non-Negative Matrix Factorization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebooks explains basic ideas behind the open source NMF implementation in [Gensim](https://github.com/RaRe-Technologies/gensim), including code examples for applying NMF to text processing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's in this tutorial?\n",
    "\n",
    "1. [Introduction: Why NMF?](#1.-Introduction-to-NMF)\n",
    "2. [Code example on 20 Newsgroups](#2.-Code-example:-NMF-on-20-Newsgroups)\n",
    "3. [Benchmarks against Sklearn's NMF and Gensim's LDA](#3.-Benchmarks)\n",
    "4. [Large-scale NMF training on the English Wikipedia (sparse text vectors)](#4.-NMF-on-English-Wikipedia)\n",
    "5. [NMF on face decomposition (dense image vectors)](#5.-And-now-for-something-completely-different:-Face-decomposition-from-images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "# 1. Introduction to NMF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## What's in a name?\n",
    "\n",
    "Gensim's Online Non-Negative Matrix Factorization (NMF, NNMF, ONMF) implementation is based on [Renbo Zhao, Vincent Y. F. Tan: Online Nonnegative Matrix Factorization with Outliers, 2016](https://arxiv.org/abs/1604.02634) and is optimized for extremely large, sparse, streamed inputs. Such inputs happen in NLP with **unsupervised training** on massive text corpora.\n",
    "\n",
    "* Why **Online**? Because corpora and datasets in modern ML can be very large, and RAM is limited. Unlike batch algorithms, online algorithms learn iteratively, streaming through the available training examples, without loading the entire dataset into RAM or requiring random-access to the data examples.\n",
    "\n",
    "* Why **Non-Negative**? Because non-negativity leads to more interpretable, sparse \"human-friendly\" topics. This is in contrast to e.g. SVD (another popular matrix factorization method with [super-efficient implementation in Gensim](https://radimrehurek.com/gensim/models/lsimodel.html)), which produces dense negative factors and thus harder-to-interpret topics.\n",
    "\n",
    "* **Matrix factorizations** are the corner stone of modern machine learning. They can be used either directly (recommendation systems, bi-clustering, image compression, topic modeling…) or as internal routines in more complex deep learning algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## How ONNMF works\n",
    "\n",
    "Terminology:\n",
    "- `corpus` is a stream of input documents = training examples\n",
    "- `batch` is a chunk of input corpus, a word-document matrix mini-batch that fits in RAM\n",
    "- `W` is a word-topic matrix (to be learned; stored in the resulting model)\n",
    "- `h` is a topic-document matrix (to be learned; not stored, but rather inferred for documents on-the-fly)\n",
    "- `A`, `B` - matrices that accumulate information from consecutive chunks. `A = h.dot(ht)`, `B = v.dot(ht)`.\n",
    "\n",
    "The idea behind the algorithm is as follows:\n",
    "\n",
    "```\n",
    "    Initialize W, A and B matrices\n",
    "\n",
    "    for batch in input corpus batches:\n",
    "        infer h:\n",
    "            do coordinate gradient descent step to find h that minimizes ||batch - Wh|| in L2 norm\n",
    "\n",
    "            bound h so that it is non-negative\n",
    "\n",
    "        update A and B:\n",
    "            A = h.dot(ht)\n",
    "            B = batch.dot(ht)\n",
    "\n",
    "        update W:\n",
    "            do gradient descent step to find W that minimizes ||0.5*trace(WtWA) - trace(WtB)|| in L2 norm\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Code example: NMF on 20 Newsgroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's import the models we'll be using throughout this tutorial (`numpy==1.14.2`, `matplotlib==3.0.2`, `pandas==0.24.1`, `sklearn==0.19.1`, `gensim==3.7.1`) and set up logging at INFO level.\n",
    "\n",
    "Gensim uses logging generously to inform users what's going on. Eyeballing the logs is a good sanity check, to make sure everything is working as expected.\n",
    "\n",
    "Only `numpy` and `gensim` are actually needed to train and use NMF. The other imports are used only to make our life a little easier in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import time\n",
    "from contextlib import contextmanager\n",
    "import os\n",
    "from multiprocessing import Process\n",
    "import psutil\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import RandomState\n",
    "from sklearn import decomposition\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.datasets import fetch_olivetti_faces\n",
    "from sklearn.decomposition.nmf import NMF as SklearnNmf\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "import gensim.downloader\n",
    "from gensim import matutils, utils\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import CoherenceModel, LdaModel, TfidfModel\n",
    "from gensim.models.basemodel import BaseTopicModel\n",
    "from gensim.models.nmf import Nmf as GensimNmf\n",
    "from gensim.parsing.preprocessing import preprocess_string\n",
    "\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load the notorious [20 Newsgroups dataset](http://qwone.com/~jason/20Newsgroups/) from Gensim's [repository of pre-trained models and corpora](https://github.com/RaRe-Technologies/gensim-data):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "newsgroups = gensim.downloader.load('20-newsgroups')\n",
    "\n",
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'comp.graphics',\n",
    "    'rec.motorcycles',\n",
    "    'talk.politics.mideast',\n",
    "    'sci.space'\n",
    "]\n",
    "\n",
    "categories = {name: idx for idx, name in enumerate(categories)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a train/test split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_state = RandomState(42)\n",
    "\n",
    "trainset = np.array([\n",
    "    {\n",
    "        'data': doc['data'],\n",
    "        'target': categories[doc['topic']],\n",
    "    }\n",
    "    for doc in newsgroups\n",
    "    if doc['topic'] in categories and doc['set'] == 'train'\n",
    "])\n",
    "random_state.shuffle(trainset)\n",
    "\n",
    "testset = np.array([\n",
    "    {\n",
    "        'data': doc['data'],\n",
    "        'target': categories[doc['topic']],\n",
    "    }\n",
    "    for doc in newsgroups\n",
    "    if doc['topic'] in categories and doc['set'] == 'test'\n",
    "])\n",
    "random_state.shuffle(testset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use very [simple preprocessing with stemming](https://radimrehurek.com/gensim/parsing/preprocessing.html#gensim.parsing.preprocessing.preprocess_string) to tokenize each document. YMMV; in your application, use whatever preprocessing makes sense in your domain. Correctly preparing the input has [major impact](https://en.wikipedia.org/wiki/Garbage_in,_garbage_out) on any subsequent ML training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_documents = [preprocess_string(doc['data']) for doc in trainset]\n",
    "test_documents = [preprocess_string(doc['data']) for doc in testset]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dictionary compilation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a mapping between tokens and their ids. Another option would be a [HashDictionary](https://radimrehurek.com/gensim/corpora/hashdictionary.html), saving ourselves one pass over the training documents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 01:54:20,844 : INFO : adding document #0 to Dictionary(0 unique tokens: [])\n",
      "2019-03-04 01:54:21,153 : INFO : built Dictionary(25279 unique tokens: ['gladli', 'garrett', 'stuck', 'gov', 'karasi']...) from 2819 documents (total 435328 corpus positions)\n",
      "2019-03-04 01:54:21,182 : INFO : discarding 18198 tokens: [('batka', 1), ('batkaj', 1), ('beatl', 1), ('ccmail', 3), ('dayton', 4), ('edu', 1785), ('inhibit', 1), ('jbatka', 1), ('line', 2748), ('organ', 2602)]...\n",
      "2019-03-04 01:54:21,183 : INFO : keeping 7081 tokens which were in no less than 5 and no more than 1409 (=50.0%) documents\n",
      "2019-03-04 01:54:21,193 : INFO : resulting dictionary: Dictionary(7081 unique tokens: ['gladli', 'run', 'trillion', 'stuck', 'order']...)\n"
     ]
    }
   ],
   "source": [
    "dictionary = Dictionary(train_documents)\n",
    "dictionary.filter_extremes(no_below=5, no_above=0.5, keep_n=20000)  # filter out too in/frequent tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's vectorize the training corpus into the bag-of-words format. We'll train LDA on a BOW and NMFs on an TF-IDF corpus:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfModel(dictionary=dictionary)\n",
    "\n",
    "train_corpus = [\n",
    "    dictionary.doc2bow(document)\n",
    "    for document\n",
    "    in train_documents\n",
    "]\n",
    "\n",
    "test_corpus = [\n",
    "    dictionary.doc2bow(document)\n",
    "    for document\n",
    "    in test_documents\n",
    "]\n",
    "\n",
    "train_corpus_tfidf = list(tfidf[train_corpus])\n",
    "\n",
    "test_corpus_tfidf = list(tfidf[test_corpus])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we simply stored the bag-of-words vectors into a `list`, but Gensim accepts [any iterable](https://radimrehurek.com/gensim/tut1.html#corpus-streaming-one-document-at-a-time) as input, including streamed ones. To learn more about memory-efficient input iterables, see our [Data Streaming in Python: Generators, Iterators, Iterables](https://rare-technologies.com/data-streaming-in-python-generators-iterators-iterables/) tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NMF Model Training\n",
    "\n",
    "The API works in the same way as other Gensim models, such as [LdaModel](https://radimrehurek.com/gensim/models/ldamodel.html) or [LsiModel](https://radimrehurek.com/gensim/models/lsimodel.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notable model parameters:\n",
    "\n",
    "- `kappa` float, optional\n",
    "\n",
    "    Gradient descent step size.\n",
    "    Larger value makes the model train faster, but could lead to non-convergence if set too large.\n",
    "    \n",
    "- `w_max_iter` int, optional\n",
    "\n",
    "    Maximum number of iterations to train W per each batch.\n",
    "    \n",
    "- `w_stop_condition` float, optional\n",
    "\n",
    "    If the error difference gets smaller than this, training of ``W`` stops for the current batch.\n",
    "    \n",
    "- `h_r_max_iter` int, optional\n",
    "\n",
    "    Maximum number of iterations to train h per each batch.\n",
    "    \n",
    "- `h_r_stop_condition` float, optional\n",
    "\n",
    "    If the error difference gets smaller than this, training of ``h`` stops for the current batch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn an NMF model with 5 topics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 01:54:24,559 : INFO : running NMF training, 5 topics, 5 passes over the supplied corpus of 2819 documents, evaluating l2 norm every 2819 documents\n",
      "2019-03-04 01:54:24,574 : INFO : PROGRESS: pass 0, at document #1000/2819\n",
      "2019-03-04 01:54:24,589 : INFO : W error diff: -inf\n",
      "2019-03-04 01:54:24,604 : INFO : PROGRESS: pass 0, at document #2000/2819\n",
      "2019-03-04 01:54:24,615 : INFO : W error diff: -2.1783593508632997\n",
      "2019-03-04 01:54:24,626 : INFO : PROGRESS: pass 0, at document #2819/2819\n",
      "2019-03-04 01:54:24,722 : INFO : L2 norm: 28.137070033533682\n",
      "2019-03-04 01:54:24,756 : INFO : topic #0 (0.404): 0.011*\"isra\" + 0.010*\"israel\" + 0.007*\"arab\" + 0.006*\"jew\" + 0.005*\"palestinian\" + 0.004*\"henri\" + 0.003*\"toronto\" + 0.003*\"question\" + 0.003*\"kill\" + 0.003*\"polici\"\n",
      "2019-03-04 01:54:24,757 : INFO : topic #1 (0.358): 0.009*\"space\" + 0.005*\"access\" + 0.005*\"nasa\" + 0.004*\"pat\" + 0.003*\"digex\" + 0.003*\"orbit\" + 0.003*\"shuttl\" + 0.003*\"graphic\" + 0.003*\"data\" + 0.003*\"com\"\n",
      "2019-03-04 01:54:24,757 : INFO : topic #2 (0.388): 0.013*\"armenian\" + 0.006*\"turkish\" + 0.005*\"greek\" + 0.005*\"peopl\" + 0.004*\"armenia\" + 0.004*\"turk\" + 0.004*\"argic\" + 0.004*\"bike\" + 0.003*\"serdar\" + 0.003*\"turkei\"\n",
      "2019-03-04 01:54:24,758 : INFO : topic #3 (0.423): 0.010*\"moral\" + 0.006*\"keith\" + 0.004*\"anim\" + 0.004*\"jake\" + 0.003*\"boni\" + 0.003*\"act\" + 0.003*\"instinct\" + 0.003*\"think\" + 0.003*\"caltech\" + 0.003*\"object\"\n",
      "2019-03-04 01:54:24,759 : INFO : topic #4 (0.441): 0.009*\"islam\" + 0.009*\"god\" + 0.006*\"muslim\" + 0.006*\"livesei\" + 0.005*\"imag\" + 0.005*\"sgi\" + 0.005*\"jaeger\" + 0.004*\"jon\" + 0.004*\"solntz\" + 0.004*\"wpd\"\n",
      "2019-03-04 01:54:24,765 : INFO : W error diff: -0.6087333117616911\n",
      "2019-03-04 01:54:24,779 : INFO : PROGRESS: pass 1, at document #1000/2819\n",
      "2019-03-04 01:54:24,787 : INFO : W error diff: -1.5858439279007879\n",
      "2019-03-04 01:54:24,801 : INFO : PROGRESS: pass 1, at document #2000/2819\n",
      "2019-03-04 01:54:24,807 : INFO : W error diff: -1.1329837530094071\n",
      "2019-03-04 01:54:24,820 : INFO : PROGRESS: pass 1, at document #2819/2819\n",
      "2019-03-04 01:54:24,914 : INFO : L2 norm: 28.02006726219276\n",
      "2019-03-04 01:54:24,947 : INFO : topic #0 (0.345): 0.014*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.007*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.003*\"polici\" + 0.003*\"attack\" + 0.003*\"henri\"\n",
      "2019-03-04 01:54:24,947 : INFO : topic #1 (0.253): 0.008*\"space\" + 0.005*\"nasa\" + 0.004*\"access\" + 0.003*\"orbit\" + 0.003*\"pat\" + 0.003*\"digex\" + 0.003*\"launch\" + 0.003*\"shuttl\" + 0.003*\"graphic\" + 0.003*\"com\"\n",
      "2019-03-04 01:54:24,948 : INFO : topic #2 (0.299): 0.020*\"armenian\" + 0.010*\"turkish\" + 0.007*\"armenia\" + 0.006*\"turk\" + 0.006*\"argic\" + 0.006*\"serdar\" + 0.005*\"greek\" + 0.005*\"turkei\" + 0.004*\"genocid\" + 0.004*\"peopl\"\n",
      "2019-03-04 01:54:24,949 : INFO : topic #3 (0.353): 0.013*\"moral\" + 0.011*\"keith\" + 0.006*\"object\" + 0.005*\"caltech\" + 0.005*\"schneider\" + 0.004*\"anim\" + 0.004*\"allan\" + 0.004*\"cco\" + 0.004*\"jake\" + 0.004*\"boni\"\n",
      "2019-03-04 01:54:24,949 : INFO : topic #4 (0.380): 0.011*\"islam\" + 0.011*\"god\" + 0.006*\"livesei\" + 0.006*\"sgi\" + 0.006*\"jaeger\" + 0.005*\"muslim\" + 0.005*\"jon\" + 0.005*\"religion\" + 0.004*\"imag\" + 0.004*\"solntz\"\n",
      "2019-03-04 01:54:24,953 : INFO : W error diff: -0.05304441334403265\n",
      "2019-03-04 01:54:24,967 : INFO : PROGRESS: pass 2, at document #1000/2819\n",
      "2019-03-04 01:54:24,973 : INFO : W error diff: -0.6532464912217009\n",
      "2019-03-04 01:54:24,988 : INFO : PROGRESS: pass 2, at document #2000/2819\n",
      "2019-03-04 01:54:24,993 : INFO : W error diff: -0.5542774416923812\n",
      "2019-03-04 01:54:25,005 : INFO : PROGRESS: pass 2, at document #2819/2819\n",
      "2019-03-04 01:54:25,099 : INFO : L2 norm: 27.999892226543682\n",
      "2019-03-04 01:54:25,132 : INFO : topic #0 (0.343): 0.014*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.003*\"attack\" + 0.003*\"polici\" + 0.003*\"lebanon\"\n",
      "2019-03-04 01:54:25,133 : INFO : topic #1 (0.229): 0.007*\"space\" + 0.005*\"nasa\" + 0.004*\"access\" + 0.003*\"orbit\" + 0.003*\"pat\" + 0.003*\"launch\" + 0.003*\"digex\" + 0.003*\"gov\" + 0.003*\"graphic\" + 0.003*\"com\"\n",
      "2019-03-04 01:54:25,134 : INFO : topic #2 (0.283): 0.022*\"armenian\" + 0.011*\"turkish\" + 0.007*\"armenia\" + 0.007*\"turk\" + 0.007*\"argic\" + 0.007*\"serdar\" + 0.006*\"turkei\" + 0.005*\"greek\" + 0.005*\"genocid\" + 0.004*\"soviet\"\n",
      "2019-03-04 01:54:25,134 : INFO : topic #3 (0.347): 0.015*\"moral\" + 0.013*\"keith\" + 0.007*\"object\" + 0.006*\"caltech\" + 0.005*\"schneider\" + 0.005*\"allan\" + 0.005*\"cco\" + 0.004*\"anim\" + 0.004*\"jake\" + 0.004*\"natur\"\n",
      "2019-03-04 01:54:25,135 : INFO : topic #4 (0.365): 0.011*\"god\" + 0.011*\"islam\" + 0.006*\"livesei\" + 0.006*\"sgi\" + 0.006*\"jaeger\" + 0.005*\"muslim\" + 0.005*\"religion\" + 0.005*\"jon\" + 0.005*\"atheist\" + 0.004*\"atheism\"\n",
      "2019-03-04 01:54:25,138 : INFO : W error diff: 0.06399021760879364\n",
      "2019-03-04 01:54:25,151 : INFO : PROGRESS: pass 3, at document #1000/2819\n",
      "2019-03-04 01:54:25,157 : INFO : W error diff: -0.3678424933365889\n",
      "2019-03-04 01:54:25,172 : INFO : PROGRESS: pass 3, at document #2000/2819\n",
      "2019-03-04 01:54:25,177 : INFO : W error diff: -0.34924666183303543\n",
      "2019-03-04 01:54:25,189 : INFO : PROGRESS: pass 3, at document #2819/2819\n",
      "2019-03-04 01:54:25,283 : INFO : L2 norm: 27.991268049236886\n",
      "2019-03-04 01:54:25,315 : INFO : topic #0 (0.350): 0.015*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.003*\"attack\" + 0.003*\"lebanon\" + 0.003*\"polici\"\n",
      "2019-03-04 01:54:25,316 : INFO : topic #1 (0.220): 0.007*\"space\" + 0.005*\"nasa\" + 0.003*\"access\" + 0.003*\"orbit\" + 0.003*\"launch\" + 0.003*\"pat\" + 0.003*\"gov\" + 0.003*\"com\" + 0.003*\"digex\" + 0.002*\"alaska\"\n",
      "2019-03-04 01:54:25,317 : INFO : topic #2 (0.282): 0.023*\"armenian\" + 0.011*\"turkish\" + 0.007*\"armenia\" + 0.007*\"turk\" + 0.007*\"argic\" + 0.007*\"serdar\" + 0.006*\"turkei\" + 0.005*\"greek\" + 0.005*\"genocid\" + 0.005*\"soviet\"\n",
      "2019-03-04 01:54:25,317 : INFO : topic #3 (0.351): 0.016*\"moral\" + 0.015*\"keith\" + 0.007*\"object\" + 0.007*\"caltech\" + 0.006*\"schneider\" + 0.005*\"allan\" + 0.005*\"cco\" + 0.004*\"anim\" + 0.004*\"natur\" + 0.004*\"think\"\n",
      "2019-03-04 01:54:25,318 : INFO : topic #4 (0.364): 0.012*\"god\" + 0.011*\"islam\" + 0.006*\"sgi\" + 0.006*\"jaeger\" + 0.006*\"livesei\" + 0.005*\"muslim\" + 0.005*\"religion\" + 0.005*\"atheist\" + 0.005*\"atheism\" + 0.004*\"jon\"\n",
      "2019-03-04 01:54:25,321 : INFO : W error diff: 0.08877110840856872\n",
      "2019-03-04 01:54:25,334 : INFO : PROGRESS: pass 4, at document #1000/2819\n",
      "2019-03-04 01:54:25,339 : INFO : W error diff: -0.2446709705343757\n",
      "2019-03-04 01:54:25,354 : INFO : PROGRESS: pass 4, at document #2000/2819\n",
      "2019-03-04 01:54:25,359 : INFO : W error diff: -0.24931839405260803\n",
      "2019-03-04 01:54:25,371 : INFO : PROGRESS: pass 4, at document #2819/2819\n",
      "2019-03-04 01:54:25,465 : INFO : L2 norm: 27.98648818098989\n",
      "2019-03-04 01:54:25,498 : INFO : topic #0 (0.354): 0.015*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.004*\"attack\" + 0.003*\"lebanon\" + 0.003*\"polici\"\n",
      "2019-03-04 01:54:25,498 : INFO : topic #1 (0.209): 0.007*\"space\" + 0.005*\"nasa\" + 0.003*\"access\" + 0.003*\"orbit\" + 0.003*\"launch\" + 0.003*\"gov\" + 0.003*\"pat\" + 0.003*\"com\" + 0.002*\"alaska\" + 0.002*\"moon\"\n",
      "2019-03-04 01:54:25,499 : INFO : topic #2 (0.283): 0.023*\"armenian\" + 0.011*\"turkish\" + 0.008*\"armenia\" + 0.007*\"argic\" + 0.007*\"turk\" + 0.007*\"serdar\" + 0.006*\"turkei\" + 0.005*\"greek\" + 0.005*\"genocid\" + 0.005*\"soviet\"\n",
      "2019-03-04 01:54:25,500 : INFO : topic #3 (0.356): 0.017*\"moral\" + 0.016*\"keith\" + 0.007*\"object\" + 0.007*\"caltech\" + 0.006*\"schneider\" + 0.006*\"allan\" + 0.006*\"cco\" + 0.004*\"anim\" + 0.004*\"natur\" + 0.004*\"goal\"\n",
      "2019-03-04 01:54:25,500 : INFO : topic #4 (0.366): 0.012*\"god\" + 0.011*\"islam\" + 0.006*\"jaeger\" + 0.005*\"sgi\" + 0.005*\"livesei\" + 0.005*\"muslim\" + 0.005*\"atheist\" + 0.005*\"religion\" + 0.005*\"atheism\" + 0.004*\"rushdi\"\n",
      "2019-03-04 01:54:25,503 : INFO : W error diff: 0.0932956490045207\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.52 s, sys: 1.84 s, total: 3.36 s\n",
      "Wall time: 944 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "nmf = GensimNmf(\n",
    "    corpus=train_corpus_tfidf,\n",
    "    num_topics=5,\n",
    "    id2word=dictionary,\n",
    "    chunksize=1000,\n",
    "    passes=5,\n",
    "    eval_every=10,\n",
    "    minimum_probability=0,\n",
    "    random_state=0,\n",
    "    kappa=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View the learned topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.015*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.004*\"attack\" + 0.004*\"lebanon\" + 0.003*\"polici\"'),\n",
       " (1,\n",
       "  '0.007*\"space\" + 0.005*\"nasa\" + 0.003*\"access\" + 0.003*\"orbit\" + 0.003*\"launch\" + 0.003*\"gov\" + 0.003*\"pat\" + 0.003*\"com\" + 0.002*\"alaska\" + 0.002*\"moon\"'),\n",
       " (2,\n",
       "  '0.023*\"armenian\" + 0.012*\"turkish\" + 0.008*\"armenia\" + 0.007*\"argic\" + 0.007*\"turk\" + 0.007*\"serdar\" + 0.006*\"turkei\" + 0.005*\"greek\" + 0.005*\"genocid\" + 0.005*\"soviet\"'),\n",
       " (3,\n",
       "  '0.017*\"moral\" + 0.016*\"keith\" + 0.008*\"object\" + 0.007*\"caltech\" + 0.006*\"schneider\" + 0.006*\"allan\" + 0.006*\"cco\" + 0.004*\"anim\" + 0.004*\"natur\" + 0.004*\"goal\"'),\n",
       " (4,\n",
       "  '0.012*\"god\" + 0.011*\"islam\" + 0.006*\"jaeger\" + 0.005*\"sgi\" + 0.005*\"livesei\" + 0.005*\"muslim\" + 0.005*\"atheist\" + 0.005*\"religion\" + 0.005*\"atheism\" + 0.004*\"rushdi\"')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nmf.show_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation measure: Coherence\n",
    "\n",
    "[Topic coherence](http://qpleple.com/topic-coherence-to-evaluate-topic-models/) measures how often do most frequent tokens from each topic co-occur in one document. Larger is better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 01:54:25,582 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-4.045883079644641"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CoherenceModel(\n",
    "    model=nmf,\n",
    "    corpus=test_corpus_tfidf,\n",
    "    coherence='u_mass'\n",
    ").get_coherence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Topic inference on new documents\n",
    "\n",
    "With the NMF model trained, let's fetch one news document not seen during training, and infer its topic vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: spl@ivem.ucsd.edu (Steve Lamont)\n",
      "Subject: Re: RGB to HVS, and back\n",
      "Organization: University of Calif., San Diego/Microscopy and Imaging Resource\n",
      "Lines: 18\n",
      "Distribution: world\n",
      "NNTP-Posting-Host: ivem.ucsd.edu\n",
      "\n",
      "In article <ltu4buINNe7j@caspian.usc.edu> zyeh@caspian.usc.edu (zhenghao yeh) writes:\n",
      ">|> See Foley, van Dam, Feiner, and Hughes, _Computer Graphics: Principles\n",
      ">|> and Practice, Second Edition_.\n",
      ">|> \n",
      ">|> [If people would *read* this book, 75 percent of the questions in this\n",
      ">|> froup would disappear overnight...]\n",
      ">|> \n",
      ">\tNot really. I think it is less than 10%.\n",
      "\n",
      "Nah... I figure most people would be so busy reading that they wouldn't\n",
      "have *time* to post. :-) :-) :-)\n",
      "\n",
      "\t\t\t\t\t\t\tspl\n",
      "-- \n",
      "Steve Lamont, SciViGuy -- (619) 534-7968 -- spl@szechuan.ucsd.edu\n",
      "San Diego Microscopy and Imaging Resource/UC San Diego/La Jolla, CA 92093-0608\n",
      "\"Until I meet you, then, in Upper Hell\n",
      "Convulsed, foaming immortal blood: farewell\" - J. Berryman, \"A Professor's Song\"\n",
      "\n",
      "====================================================================================================\n",
      "Topics: [(0, 0.10094349983895379), (1, 0.40527834628482196), (2, 0.14330724750919113), (3, 0.02887286985628184), (4, 0.32159803651075125)]\n"
     ]
    }
   ],
   "source": [
    "print(testset[0]['data'])\n",
    "print('=' * 100)\n",
    "print(\"Topics: {}\".format(nmf[test_corpus[0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word topic inference\n",
    "\n",
    "Similarly, we can inspect the topic distribution assigned to a vocabulary term:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Word: actual\n",
      "Topics: [(0, 0.1517466731147538), (1, 0.2824521057319929), (2, 0.042590027339691805), (3, 0.2520757387076886), (4, 0.2711354551058729)]\n"
     ]
    }
   ],
   "source": [
    "word = dictionary[0]\n",
    "print(\"Word: {}\".format(word))\n",
    "print(\"Topics: {}\".format(nmf.get_term_topics(word)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Internal NMF state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Density is a fraction of non-zero elements in a matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "def density(matrix):\n",
    "    return (matrix > 0).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Term-topic matrix of shape `(words, topics)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.6864567151532269\n"
     ]
    }
   ],
   "source": [
    "print(\"Density: {}\".format(density(nmf._W)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Topic-document matrix for the last batch of shape `(topics, batch)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.662026862026862\n"
     ]
    }
   ],
   "source": [
    "print(\"Density: {}\".format(density(nmf._h)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Benchmarks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gensim NMF vs Sklearn NMF vs Gensim LDA\n",
    "\n",
    "We'll run these three unsupervised models on the [20newsgroups](https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html) dataset.\n",
    "\n",
    "20 Newsgroups also contains labels for each document, which will allow us to evaluate the trained models on an \"upstream\" classification task, using the unsupervised document topics as input features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "\n",
    "We'll track these metrics as we train and test NMF on the 20-newsgroups corpus we created above:\n",
    "- `train time` - time to train a model\n",
    "- `mean_ram` - mean RAM consumption during training\n",
    "- `max_ram` - maximum RAM consumption during training\n",
    "- `train time` - time to train a model.\n",
    "- `coherence` - coherence score (larger is better).\n",
    "- `l2_norm` - L2 norm of `v - Wh` (less is better, not defined for LDA).\n",
    "- `f1` - [F1 score](https://en.wikipedia.org/wiki/F1_score) on the task of news topic classification (larger is better)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "fixed_params = dict(\n",
    "    chunksize=1000,\n",
    "    num_topics=5,\n",
    "    id2word=dictionary,\n",
    "    passes=5,\n",
    "    eval_every=10,\n",
    "    minimum_probability=0,\n",
    "    random_state=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "@contextmanager\n",
    "def measure_ram(output, tick=5):\n",
    "    def _measure_ram(pid, output, tick=tick):\n",
    "        py = psutil.Process(pid)\n",
    "        with open(output, 'w') as outfile:\n",
    "            while True:\n",
    "                memory = py.memory_info().rss\n",
    "                outfile.write(\"{}\\n\".format(memory))\n",
    "                outfile.flush()\n",
    "                time.sleep(tick)\n",
    "\n",
    "    pid = os.getpid()\n",
    "    p = Process(target=_measure_ram, args=(pid, output, tick))\n",
    "    p.start()\n",
    "    yield\n",
    "    p.terminate()\n",
    "\n",
    "\n",
    "def get_train_time_and_ram(func, name, tick=5):\n",
    "    memprof_filename = \"{}.memprof\".format(name)\n",
    "\n",
    "    start = time.time()\n",
    "\n",
    "    with measure_ram(memprof_filename, tick=tick):\n",
    "        result = func()\n",
    "\n",
    "    elapsed_time = pd.to_timedelta(time.time() - start, unit='s').round('s')\n",
    "\n",
    "    memprof_df = pd.read_csv(memprof_filename, squeeze=True)\n",
    "\n",
    "    mean_ram = \"{} MB\".format(\n",
    "        int(memprof_df.mean() // 2 ** 20),\n",
    "    )\n",
    "\n",
    "    max_ram = \"{} MB\".format(int(memprof_df.max() // 2 ** 20))\n",
    "\n",
    "    return elapsed_time, mean_ram, max_ram, result\n",
    "\n",
    "\n",
    "def get_f1(model, train_corpus, X_test, y_train, y_test):\n",
    "    if isinstance(model, SklearnNmf):\n",
    "        dense_train_corpus = matutils.corpus2dense(\n",
    "            train_corpus,\n",
    "            num_terms=model.components_.shape[1],\n",
    "        )\n",
    "        X_train = model.transform(dense_train_corpus.T)\n",
    "    else:\n",
    "        X_train = np.zeros((len(train_corpus), model.num_topics))\n",
    "        for bow_id, bow in enumerate(train_corpus):\n",
    "            for topic_id, word_count in model.get_document_topics(bow):\n",
    "                X_train[bow_id, topic_id] = word_count\n",
    "\n",
    "    log_reg = LogisticRegressionCV(multi_class='multinomial', cv=5)\n",
    "    log_reg.fit(X_train, y_train)\n",
    "\n",
    "    pred_labels = log_reg.predict(X_test)\n",
    "\n",
    "    return f1_score(y_test, pred_labels, average='micro')\n",
    "\n",
    "def get_sklearn_topics(model, top_n=5):\n",
    "    topic_probas = model.components_.T\n",
    "    topic_probas = topic_probas / topic_probas.sum(axis=0)\n",
    "\n",
    "    sparsity = np.zeros(topic_probas.shape[1])\n",
    "\n",
    "    for row in topic_probas:\n",
    "        sparsity += (row == 0)\n",
    "\n",
    "    sparsity /= topic_probas.shape[1]\n",
    "\n",
    "    topic_probas = topic_probas[:, sparsity.argsort()[::-1]][:, :top_n]\n",
    "\n",
    "    token_indices = topic_probas.argsort(axis=0)[:-11:-1, :]\n",
    "    topic_probas.sort(axis=0)\n",
    "    topic_probas = topic_probas[:-11:-1, :]\n",
    "\n",
    "    topics = []\n",
    "\n",
    "    for topic_idx in range(topic_probas.shape[1]):\n",
    "        tokens = [\n",
    "            model.id2word[token_idx]\n",
    "            for token_idx\n",
    "            in token_indices[:, topic_idx]\n",
    "        ]\n",
    "        topic = (\n",
    "            '{}*\"{}\"'.format(round(proba, 3), token)\n",
    "            for proba, token\n",
    "            in zip(topic_probas[:, topic_idx], tokens)\n",
    "        )\n",
    "        topic = \" + \".join(topic)\n",
    "        topics.append((topic_idx, topic))\n",
    "\n",
    "    return topics\n",
    "\n",
    "def get_metrics(model, test_corpus, train_corpus=None, y_train=None, y_test=None, dictionary=None):\n",
    "    if isinstance(model, SklearnNmf):\n",
    "        model.get_topics = lambda: model.components_\n",
    "        model.show_topics = lambda top_n: get_sklearn_topics(model, top_n)\n",
    "        model.id2word = dictionary\n",
    "\n",
    "    W = model.get_topics().T\n",
    "\n",
    "    dense_test_corpus = matutils.corpus2dense(\n",
    "        test_corpus,\n",
    "        num_terms=W.shape[0],\n",
    "    )\n",
    "\n",
    "    if isinstance(model, SklearnNmf):\n",
    "        H = model.transform(dense_test_corpus.T).T\n",
    "    else:\n",
    "        H = np.zeros((model.num_topics, len(test_corpus)))\n",
    "        for bow_id, bow in enumerate(test_corpus):\n",
    "            for topic_id, word_count in model.get_document_topics(bow):\n",
    "                H[topic_id, bow_id] = word_count\n",
    "\n",
    "    l2_norm = None\n",
    "\n",
    "    if not isinstance(model, LdaModel):\n",
    "        pred_factors = W.dot(H)\n",
    "\n",
    "        l2_norm = np.linalg.norm(pred_factors - dense_test_corpus)\n",
    "        l2_norm = round(l2_norm, 4)\n",
    "\n",
    "    f1 = None\n",
    "\n",
    "    if train_corpus and y_train and y_test:\n",
    "        f1 = get_f1(model, train_corpus, H.T, y_train, y_test)\n",
    "        f1 = round(f1, 4)\n",
    "\n",
    "    model.normalize = True\n",
    "\n",
    "    coherence = CoherenceModel(\n",
    "        model=model,\n",
    "        corpus=test_corpus,\n",
    "        coherence='u_mass'\n",
    "    ).get_coherence()\n",
    "    coherence = round(coherence, 4)\n",
    "\n",
    "    topics = model.show_topics(5)\n",
    "\n",
    "    model.normalize = False\n",
    "\n",
    "    return dict(\n",
    "        coherence=coherence,\n",
    "        l2_norm=l2_norm,\n",
    "        f1=f1,\n",
    "        topics=topics,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tm_metrics = pd.DataFrame(columns=['model', 'train_time', 'coherence', 'l2_norm', 'f1', 'topics'])\n",
    "\n",
    "y_train = [doc['target'] for doc in trainset]\n",
    "y_test = [doc['target'] for doc in testset]\n",
    "\n",
    "# LDA metrics\n",
    "row = {}\n",
    "row['model'] = 'lda'\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], lda = get_train_time_and_ram(\n",
    "    lambda: LdaModel(\n",
    "        corpus=train_corpus,\n",
    "        **fixed_params,\n",
    "    ),\n",
    "    'lda',\n",
    "    1,\n",
    ")\n",
    "row.update(get_metrics(\n",
    "    lda, test_corpus, train_corpus, y_train, y_test,\n",
    "))\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n",
    "\n",
    "# Sklearn NMF metrics\n",
    "row = {}\n",
    "row['model'] = 'sklearn_nmf'\n",
    "train_dense_corpus_tfidf = matutils.corpus2dense(train_corpus_tfidf, len(dictionary)).T\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], sklearn_nmf = get_train_time_and_ram(\n",
    "    lambda: SklearnNmf(n_components=5, random_state=42).fit(train_dense_corpus_tfidf),\n",
    "    'sklearn_nmf',\n",
    "    1,\n",
    ")\n",
    "row.update(get_metrics(\n",
    "    sklearn_nmf, test_corpus_tfidf, train_corpus_tfidf, y_train, y_test, dictionary,\n",
    "))\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n",
    "\n",
    "# Gensim NMF metrics\n",
    "row = {}\n",
    "row['model'] = 'gensim_nmf'\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], gensim_nmf = get_train_time_and_ram(\n",
    "    lambda: GensimNmf(\n",
    "        normalize=False,\n",
    "        corpus=train_corpus_tfidf,\n",
    "        **fixed_params\n",
    "    ),\n",
    "    'gensim_nmf',\n",
    "    0.5,\n",
    ")\n",
    "row.update(get_metrics(\n",
    "    gensim_nmf, test_corpus_tfidf, train_corpus_tfidf, y_train, y_test,\n",
    "))\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)\n",
    "tm_metrics.replace(np.nan, '-', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>train_time</th>\n",
       "      <th>coherence</th>\n",
       "      <th>l2_norm</th>\n",
       "      <th>f1</th>\n",
       "      <th>max_ram</th>\n",
       "      <th>mean_ram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lda</td>\n",
       "      <td>00:00:08</td>\n",
       "      <td>-2.1054</td>\n",
       "      <td>-</td>\n",
       "      <td>0.7511</td>\n",
       "      <td>288 MB</td>\n",
       "      <td>288 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sklearn_nmf</td>\n",
       "      <td>00:00:02</td>\n",
       "      <td>-3.1835</td>\n",
       "      <td>42.4759</td>\n",
       "      <td>0.7900</td>\n",
       "      <td>824 MB</td>\n",
       "      <td>692 MB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gensim_nmf</td>\n",
       "      <td>00:00:01</td>\n",
       "      <td>-4.0459</td>\n",
       "      <td>42.5486</td>\n",
       "      <td>0.8044</td>\n",
       "      <td>427 MB</td>\n",
       "      <td>427 MB</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model train_time  coherence  l2_norm      f1 max_ram mean_ram\n",
       "0          lda   00:00:08    -2.1054        -  0.7511  288 MB   288 MB\n",
       "1  sklearn_nmf   00:00:02    -3.1835  42.4759  0.7900  824 MB   692 MB\n",
       "2   gensim_nmf   00:00:01    -4.0459  42.5486  0.8044  427 MB   427 MB"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_metrics.drop('topics', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main insights\n",
    "\n",
    "- Gensim NMF is **ridiculously fast** and leaves both LDA and Sklearn far behind in terms of training time and quality on downstream task (F1 score), though coherence is the lowest among all models.\n",
    "- Gensim NMF beats Sklearn NMF in RAM consumption, but L2 norm is a bit worse.\n",
    "- Gensim NMF consumes a bit more RAM than LDA."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learned topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's inspect the 5 topics learned by each of the three models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "lda:\n",
      "(0, '0.013*\"space\" + 0.008*\"imag\" + 0.007*\"nasa\" + 0.006*\"graphic\" + 0.006*\"program\" + 0.005*\"launch\" + 0.005*\"file\" + 0.005*\"com\" + 0.005*\"new\" + 0.004*\"orbit\"')\n",
      "(1, '0.015*\"com\" + 0.007*\"like\" + 0.007*\"nntp\" + 0.007*\"host\" + 0.006*\"know\" + 0.006*\"univers\" + 0.005*\"henri\" + 0.005*\"work\" + 0.005*\"bit\" + 0.005*\"think\"')\n",
      "(2, '0.014*\"armenian\" + 0.011*\"peopl\" + 0.009*\"turkish\" + 0.007*\"jew\" + 0.007*\"said\" + 0.006*\"right\" + 0.005*\"know\" + 0.005*\"kill\" + 0.005*\"isra\" + 0.005*\"turkei\"')\n",
      "(3, '0.012*\"com\" + 0.010*\"israel\" + 0.009*\"bike\" + 0.006*\"isra\" + 0.006*\"dod\" + 0.005*\"like\" + 0.005*\"ride\" + 0.005*\"host\" + 0.005*\"nntp\" + 0.005*\"motorcycl\"')\n",
      "(4, '0.011*\"god\" + 0.008*\"peopl\" + 0.007*\"think\" + 0.006*\"exist\" + 0.006*\"univers\" + 0.005*\"com\" + 0.005*\"believ\" + 0.005*\"islam\" + 0.005*\"moral\" + 0.005*\"christian\"')\n",
      "\n",
      "sklearn_nmf:\n",
      "(0, '0.027*\"armenian\" + 0.013*\"turkish\" + 0.009*\"armenia\" + 0.009*\"argic\" + 0.009*\"serdar\" + 0.008*\"turk\" + 0.007*\"turkei\" + 0.006*\"genocid\" + 0.006*\"soviet\" + 0.006*\"zuma\"')\n",
      "(1, '0.015*\"israel\" + 0.014*\"isra\" + 0.01*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.005*\"jake\" + 0.005*\"boni\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.004*\"adam\"')\n",
      "(2, '0.011*\"god\" + 0.01*\"keith\" + 0.01*\"moral\" + 0.006*\"islam\" + 0.006*\"livesei\" + 0.006*\"atheist\" + 0.005*\"atheism\" + 0.005*\"caltech\" + 0.004*\"religion\" + 0.004*\"object\"')\n",
      "(3, '0.011*\"space\" + 0.008*\"nasa\" + 0.008*\"henri\" + 0.006*\"orbit\" + 0.005*\"toronto\" + 0.005*\"alaska\" + 0.005*\"launch\" + 0.005*\"moon\" + 0.004*\"gov\" + 0.004*\"access\"')\n",
      "(4, '0.005*\"bike\" + 0.005*\"graphic\" + 0.004*\"file\" + 0.004*\"imag\" + 0.003*\"com\" + 0.003*\"ride\" + 0.003*\"thank\" + 0.003*\"program\" + 0.003*\"motorcycl\" + 0.002*\"look\"')\n",
      "\n",
      "gensim_nmf:\n",
      "(0, '0.015*\"israel\" + 0.014*\"isra\" + 0.009*\"arab\" + 0.008*\"jew\" + 0.005*\"palestinian\" + 0.004*\"lebanes\" + 0.004*\"peac\" + 0.004*\"attack\" + 0.004*\"lebanon\" + 0.003*\"polici\"')\n",
      "(1, '0.007*\"space\" + 0.005*\"nasa\" + 0.003*\"access\" + 0.003*\"orbit\" + 0.003*\"launch\" + 0.003*\"gov\" + 0.003*\"pat\" + 0.003*\"com\" + 0.002*\"alaska\" + 0.002*\"moon\"')\n",
      "(2, '0.023*\"armenian\" + 0.012*\"turkish\" + 0.008*\"armenia\" + 0.007*\"argic\" + 0.007*\"turk\" + 0.007*\"serdar\" + 0.006*\"turkei\" + 0.005*\"greek\" + 0.005*\"genocid\" + 0.005*\"soviet\"')\n",
      "(3, '0.017*\"moral\" + 0.016*\"keith\" + 0.008*\"object\" + 0.007*\"caltech\" + 0.006*\"schneider\" + 0.006*\"allan\" + 0.006*\"cco\" + 0.004*\"anim\" + 0.004*\"natur\" + 0.004*\"goal\"')\n",
      "(4, '0.012*\"god\" + 0.011*\"islam\" + 0.006*\"jaeger\" + 0.005*\"sgi\" + 0.005*\"livesei\" + 0.005*\"muslim\" + 0.005*\"atheist\" + 0.005*\"religion\" + 0.005*\"atheism\" + 0.004*\"rushdi\"')\n"
     ]
    }
   ],
   "source": [
    "def compare_topics(tm_metrics):\n",
    "    for _, row in tm_metrics.iterrows():\n",
    "        print('\\n{}:'.format(row.model))\n",
    "        print(\"\\n\".join(str(topic) for topic in row.topics))\n",
    "        \n",
    "compare_topics(tm_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subjectively, Gensim and Sklearn NMFs are on par with each other, LDA looks a bit worse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. NMF on English Wikipedia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section shows how to train an NMF model on a large text corpus, the entire English Wikipedia: **2.6 billion words, in 23.1 million article sections across 5 million Wikipedia articles**.\n",
    "\n",
    "The data preprocessing takes a while, and we'll be comparing multiple models, so **reserve about FIXME hours** and some **20 GB of disk space** to go through the following notebook cells in full. You'll need `gensim>=3.7.1`, `numpy`, `tqdm`, `pandas`, `psutils`, `joblib` and `sklearn`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-import modules from scratch, so that this Section doesn't rely on any previous cells.\n",
    "import itertools\n",
    "import json\n",
    "import logging\n",
    "import time\n",
    "import os\n",
    "\n",
    "from smart_open import smart_open\n",
    "import psutil\n",
    "import numpy as np\n",
    "import scipy.sparse\n",
    "from contextlib import contextmanager, contextmanager, contextmanager\n",
    "from multiprocessing import Process\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "import joblib\n",
    "import pandas as pd\n",
    "from sklearn.decomposition.nmf import NMF as SklearnNmf\n",
    "\n",
    "import gensim.downloader\n",
    "from gensim import matutils\n",
    "from gensim.corpora import MmCorpus, Dictionary\n",
    "from gensim.models import LdaModel, LdaMulticore, CoherenceModel\n",
    "from gensim.models.nmf import Nmf as GensimNmf\n",
    "from gensim.utils import simple_preprocess\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Wikipedia dump"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll use the [gensim.downloader](https://github.com/RaRe-Technologies/gensim-data) to download a parsed Wikipedia dump (6.1 GB disk space):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = gensim.downloader.load(\"wiki-english-20171001\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the titles and sections of the first Wikipedia article, as a little sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Article: 'Anarchism'\n",
      "\n",
      "Section title: 'Introduction'\n",
      "Section text: '''Anarchism''' is a political philosophy that advocates self-governed societies based on volun…\n",
      "\n",
      "Section title: 'Etymology and terminology'\n",
      "Section text: The word ''anarchism'' is composed from the word ''anarchy'' and the suffix ''-ism'', themselves d…\n",
      "\n",
      "Section title: 'History'\n",
      "Section text: ===Origins=== Woodcut from a Diggers document by William Everard  The earliest anarchist themes ca…\n",
      "\n",
      "Section title: 'Anarchist schools of thought'\n",
      "Section text: Portrait of philosopher Pierre-Joseph Proudhon (1809–1865) by Gustave Courbet. Proudhon was the pri…\n",
      "\n",
      "Section title: 'Internal issues and debates'\n",
      "Section text: consistent with anarchist values is a controversial subject among anarchists.  Anarchism is a philo…\n",
      "\n",
      "Section title: 'Topics of interest'\n",
      "Section text: Intersecting and overlapping between various schools of thought, certain topics of interest and inte…\n",
      "\n",
      "Section title: 'Criticisms'\n",
      "Section text: Criticisms of anarchism include moral criticisms and pragmatic criticisms. Anarchism is often evalu…\n",
      "\n",
      "Section title: 'See also'\n",
      "Section text: * Anarchism by country…\n",
      "\n",
      "Section title: 'References'\n",
      "Section text: …\n",
      "\n",
      "Section title: 'Further reading'\n",
      "Section text: * Barclay, Harold, ''People Without Government: An Anthropology of Anarchy'' (2nd ed.), Left Bank Bo…\n",
      "\n",
      "Section title: 'External links'\n",
      "Section text: *  *…\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = gensim.downloader.load(\"wiki-english-20171001\")\n",
    "article = next(iter(data))\n",
    "\n",
    "print(\"Article: %r\\n\" % article['title'])\n",
    "for section_title, section_text in zip(article['section_titles'], article['section_texts']):\n",
    "    print(\"Section title: %r\" % section_title)\n",
    "    print(\"Section text: %s…\\n\" % section_text[:100].replace('\\n', ' ').strip())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a Python generator function that streams through the downloaded Wikipedia dump and preprocesses (tokenizes, lower-cases) each article:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wikidump2tokens(articles):\n",
    "    \"\"\"Stream through the Wikipedia dump, yielding a list of tokens for each article.\"\"\"\n",
    "    for article in articles:\n",
    "        article_section_texts = [\n",
    "            \" \".join([title, text])\n",
    "            for title, text\n",
    "            in zip(article['section_titles'], article['section_texts'])\n",
    "        ]\n",
    "        article_tokens = simple_preprocess(\" \".join(article_section_texts))\n",
    "        yield article_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a word-to-id mapping, in order to vectorize texts. Makes a full pass over the Wikipedia corpus, takes **~3.5 hours**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 01:54:48,345 : INFO : loading Dictionary object from wiki.dict\n",
      "2019-03-04 01:54:48,371 : INFO : loaded wiki.dict\n"
     ]
    }
   ],
   "source": [
    "if os.path.exists('wiki.dict'):\n",
    "    # If we already stored the Dictionary in a previous run, simply load it, to save time.\n",
    "    dictionary = Dictionary.load('wiki.dict')\n",
    "else:\n",
    "    dictionary = Dictionary(wikidump2tokens(data))\n",
    "    # Keep only the 30,000 most frequent vocabulary terms, after filtering away terms\n",
    "    # that are too frequent/too infrequent.\n",
    "    dictionary.filter_extremes(no_below=5, no_above=0.5, keep_n=30000)\n",
    "    dictionary.save('wiki.dict')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store preprocessed Wikipedia as bag-of-words sparse matrix in MatrixMarket format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When training NMF with a single pass over the input corpus (\"online\"), we simply vectorize each raw text straight from the input storage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_stream = (dictionary.doc2bow(article) for article in wikidump2tokens(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this tutorial though, we'll serialize (\"cache\") the vectorized bag-of-words vectors to disk, to `wiki.mm` file in MatrixMarket format. The reason is, we'll be re-using the vectorized articles multiple times, for different models for our benchmarks, and also shuffling them, so it makes sense to amortize the vectorization time by persisting the resulting vectors to disk."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, let's stream through the preprocessed sparse Wikipedia bag-of-words matrix while storing it to disk. **This step takes about 3 hours** and needs **38 GB of disk space**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomSplitCorpus(MmCorpus):\n",
    "    \"\"\"\n",
    "    Use the fact that MmCorpus supports random indexing, and create a streamed\n",
    "    corpus in shuffled order, including a train/test split for evaluation.\n",
    "    \"\"\"\n",
    "    def __init__(self, random_seed=42, testset=False, testsize=1000, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "\n",
    "        random_state = np.random.RandomState(random_seed)\n",
    "        \n",
    "        self.indices = random_state.permutation(range(self.num_docs))\n",
    "        test_nnz = sum(len(self[doc_idx]) for doc_idx in self.indices[:testsize])\n",
    "        \n",
    "        if testset:\n",
    "            self.indices = self.indices[:testsize]\n",
    "            self.num_docs = testsize\n",
    "            self.num_nnz = test_nnz\n",
    "        else:\n",
    "            self.indices = self.indices[testsize:]\n",
    "            self.num_docs -= testsize\n",
    "            self.num_nnz -= test_nnz\n",
    "\n",
    "    def __iter__(self):\n",
    "        for doc_id in self.indices:\n",
    "            yield self[doc_id]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('wiki.mm'):\n",
    "    MmCorpus.serialize('wiki.mm', vector_stream, progress_cnt=100000)\n",
    "\n",
    "if not os.path.exists('wiki_tfidf.mm'):\n",
    "    MmCorpus.serialize('wiki_tfidf.mm', tfidf[MmCorpus('wiki.mm')], progress_cnt=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 01:54:48,955 : INFO : loaded corpus index from wiki.mm.index\n",
      "2019-03-04 01:54:48,955 : INFO : initializing cython corpus reader from wiki.mm\n",
      "2019-03-04 01:54:48,957 : INFO : accepted corpus with 4924894 documents, 30000 features, 820242695 non-zero entries\n",
      "2019-03-04 01:54:53,977 : INFO : loaded corpus index from wiki.mm.index\n",
      "2019-03-04 01:54:53,979 : INFO : initializing cython corpus reader from wiki.mm\n",
      "2019-03-04 01:54:53,981 : INFO : accepted corpus with 4924894 documents, 30000 features, 820242695 non-zero entries\n",
      "2019-03-04 01:54:59,407 : INFO : loaded corpus index from wiki_tfidf.mm.index\n",
      "2019-03-04 01:54:59,407 : INFO : initializing cython corpus reader from wiki_tfidf.mm\n",
      "2019-03-04 01:54:59,408 : INFO : accepted corpus with 4924661 documents, 30000 features, 820007548 non-zero entries\n",
      "2019-03-04 01:55:02,179 : INFO : loaded corpus index from wiki_tfidf.mm.index\n",
      "2019-03-04 01:55:02,179 : INFO : initializing cython corpus reader from wiki_tfidf.mm\n",
      "2019-03-04 01:55:02,180 : INFO : accepted corpus with 4924661 documents, 30000 features, 820007548 non-zero entries\n"
     ]
    }
   ],
   "source": [
    "# Load back the vectors as two lazily-streamed train/test iterables.\n",
    "train_corpus = RandomSplitCorpus(\n",
    "    random_seed=42, testset=False, testsize=10000, fname='wiki.mm',\n",
    ")\n",
    "test_corpus = RandomSplitCorpus(\n",
    "    random_seed=42, testset=True, testsize=10000, fname='wiki.mm',\n",
    ")\n",
    "\n",
    "train_corpus_tfidf = RandomSplitCorpus(\n",
    "    random_seed=42, testset=False, testsize=10000, fname='wiki_tfidf.mm',\n",
    ")\n",
    "test_corpus_tfidf = RandomSplitCorpus(\n",
    "    random_seed=42, testset=True, testsize=10000, fname='wiki_tfidf.mm',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save preprocessed Wikipedia in scipy.sparse format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is only needed to run the Sklearn NMF on Wikipedia, for comparison in the benchmarks below. Sklearn expects in-memory scipy sparse input, not on-the-fly vector streams. Needs additional ~2 GB of disk space.\n",
    "\n",
    "\n",
    "**Skip this step if you don't need the Sklearn's NMF benchmark, and only want to run Gensim's NMF.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('wiki_train_csr.npz'):\n",
    "    scipy.sparse.save_npz(\n",
    "        'wiki_train_csr.npz',\n",
    "        matutils.corpus2csc(train_corpus_tfidf, len(dictionary)).T,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics\n",
    "\n",
    "We'll track these metrics as we train and test NMF on the Wikipedia corpus we created above:\n",
    "- `train time` - time to train a model\n",
    "- `mean_ram` - mean RAM consumption during training\n",
    "- `max_ram` - maximum RAM consumption during training\n",
    "- `train time` - time to train a model.\n",
    "- `coherence` - coherence score (larger is better).\n",
    "- `l2_norm` - L2 norm of `v - Wh` (less is better, not defined for LDA)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a dataframe in which we'll store the recorded metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm_metrics = pd.DataFrame(columns=[\n",
    "    'model', 'train_time', 'mean_ram', 'max_ram', 'coherence', 'l2_norm', 'topics',\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define common parameters, to be shared by all evaluated models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = dict(\n",
    "    chunksize=2000,\n",
    "    num_topics=50,\n",
    "    id2word=dictionary,\n",
    "    passes=1,\n",
    "    eval_every=10,\n",
    "    minimum_probability=0,\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wikipedia training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Gensim NMF model and record its metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "row = {}\n",
    "row['model'] = 'gensim_nmf'\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], nmf = get_train_time_and_ram(\n",
    "    lambda: GensimNmf(normalize=False, corpus=train_corpus_tfidf, **params),\n",
    "    'gensim_nmf',\n",
    "    1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 02:22:13,520 : INFO : saving Nmf object under gensim_nmf.model, separately None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_ram': '797 MB', 'train_time': Timedelta('0 days 00:27:09'), 'model': 'gensim_nmf', 'mean_ram': '794 MB'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 02:22:13,853 : INFO : saved gensim_nmf.model\n"
     ]
    }
   ],
   "source": [
    "print(row)\n",
    "nmf.save('gensim_nmf.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 02:22:13,859 : INFO : loading Nmf object from gensim_nmf.model\n",
      "2019-03-04 02:22:13,987 : INFO : loading id2word recursively from gensim_nmf.model.id2word.* with mmap=None\n",
      "2019-03-04 02:22:13,988 : INFO : loaded gensim_nmf.model\n",
      "2019-03-04 02:23:40,723 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n",
      "2019-03-04 02:23:40,869 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n",
      "2019-03-04 02:23:41,020 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n",
      "2019-03-04 02:23:41,169 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n",
      "2019-03-04 02:23:41,322 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n",
      "2019-03-04 02:23:41,473 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n",
      "2019-03-04 02:23:41,620 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n",
      "2019-03-04 02:23:41,764 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n",
      "2019-03-04 02:23:41,917 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n",
      "2019-03-04 02:23:42,068 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'topics': [(21, '0.009*\"his\" + 0.005*\"that\" + 0.005*\"him\" + 0.004*\"had\" + 0.003*\"they\" + 0.003*\"who\" + 0.003*\"her\" + 0.003*\"but\" + 0.003*\"king\" + 0.003*\"were\"'), (39, '0.005*\"are\" + 0.005*\"or\" + 0.004*\"be\" + 0.004*\"that\" + 0.003*\"can\" + 0.003*\"used\" + 0.003*\"this\" + 0.002*\"have\" + 0.002*\"such\" + 0.002*\"which\"'), (45, '0.091*\"apelor\" + 0.086*\"bucurești\" + 0.051*\"river\" + 0.046*\"cadastrul\" + 0.045*\"hidrologie\" + 0.045*\"meteorologie\" + 0.045*\"institutul\" + 0.045*\"române\" + 0.045*\"româniei\" + 0.045*\"rîurile\"'), (28, '0.066*\"gmina\" + 0.065*\"poland\" + 0.065*\"voivodeship\" + 0.046*\"village\" + 0.045*\"administrative\" + 0.042*\"lies\" + 0.037*\"approximately\" + 0.036*\"east\" + 0.031*\"west\" + 0.030*\"county\"'), (34, '0.087*\"romanized\" + 0.085*\"iran\" + 0.067*\"province\" + 0.067*\"rural\" + 0.066*\"census\" + 0.060*\"families\" + 0.054*\"village\" + 0.049*\"county\" + 0.047*\"population\" + 0.042*\"district\"')], 'mean_ram': '794 MB', 'l2_norm': 94.9842, 'model': 'gensim_nmf', 'max_ram': '797 MB', 'f1': None, 'train_time': Timedelta('0 days 00:27:09'), 'coherence': -2.1426}\n"
     ]
    }
   ],
   "source": [
    "nmf = GensimNmf.load('gensim_nmf.model')\n",
    "row.update(get_metrics(nmf, test_corpus_tfidf))\n",
    "print(row)\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Gensim LDA and record its metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "row = {}\n",
    "row['model'] = 'lda'\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], lda = get_train_time_and_ram(\n",
    "    lambda: LdaModel(corpus=train_corpus, **params),\n",
    "    'lda',\n",
    "    1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 03:42:49,794 : INFO : saving LdaState object under lda.model.state, separately None\n",
      "2019-03-04 03:42:49,831 : INFO : saved lda.model.state\n",
      "2019-03-04 03:42:49,856 : INFO : saving LdaModel object under lda.model, separately ['expElogbeta', 'sstats']\n",
      "2019-03-04 03:42:49,857 : INFO : not storing attribute state\n",
      "2019-03-04 03:42:49,858 : INFO : not storing attribute id2word\n",
      "2019-03-04 03:42:49,858 : INFO : not storing attribute dispatcher\n",
      "2019-03-04 03:42:49,859 : INFO : storing np array 'expElogbeta' to lda.model.expElogbeta.npy\n",
      "2019-03-04 03:42:49,865 : INFO : saved lda.model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_ram': '857 MB', 'train_time': Timedelta('0 days 01:19:07'), 'model': 'lda', 'mean_ram': '856 MB'}\n"
     ]
    }
   ],
   "source": [
    "print(row)\n",
    "lda.save('lda.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 03:42:49,870 : INFO : loading LdaModel object from lda.model\n",
      "2019-03-04 03:42:49,871 : INFO : loading expElogbeta from lda.model.expElogbeta.npy with mmap=None\n",
      "2019-03-04 03:42:49,873 : INFO : setting ignored attribute state to None\n",
      "2019-03-04 03:42:49,874 : INFO : setting ignored attribute id2word to None\n",
      "2019-03-04 03:42:49,874 : INFO : setting ignored attribute dispatcher to None\n",
      "2019-03-04 03:42:49,874 : INFO : loaded lda.model\n",
      "2019-03-04 03:42:49,875 : INFO : loading LdaState object from lda.model.state\n",
      "2019-03-04 03:42:49,907 : INFO : loaded lda.model.state\n",
      "2019-03-04 03:43:08,439 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n",
      "2019-03-04 03:43:08,563 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n",
      "2019-03-04 03:43:08,692 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n",
      "2019-03-04 03:43:08,812 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n",
      "2019-03-04 03:43:08,950 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n",
      "2019-03-04 03:43:09,076 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n",
      "2019-03-04 03:43:09,203 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n",
      "2019-03-04 03:43:09,330 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n",
      "2019-03-04 03:43:09,455 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n",
      "2019-03-04 03:43:09,586 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'topics': [(11, '0.066*\"de\" + 0.034*\"art\" + 0.030*\"french\" + 0.028*\"la\" + 0.022*\"france\" + 0.019*\"paris\" + 0.017*\"le\" + 0.016*\"museum\" + 0.013*\"van\" + 0.013*\"saint\"'), (45, '0.033*\"new\" + 0.027*\"states\" + 0.025*\"united\" + 0.023*\"york\" + 0.023*\"american\" + 0.023*\"county\" + 0.021*\"state\" + 0.017*\"city\" + 0.014*\"california\" + 0.012*\"washington\"'), (40, '0.028*\"radio\" + 0.025*\"show\" + 0.021*\"tv\" + 0.020*\"television\" + 0.016*\"news\" + 0.015*\"station\" + 0.014*\"channel\" + 0.012*\"fm\" + 0.012*\"network\" + 0.011*\"media\"'), (28, '0.064*\"university\" + 0.018*\"research\" + 0.015*\"college\" + 0.014*\"institute\" + 0.013*\"science\" + 0.011*\"professor\" + 0.010*\"has\" + 0.010*\"international\" + 0.009*\"national\" + 0.009*\"society\"'), (20, '0.179*\"he\" + 0.123*\"his\" + 0.015*\"born\" + 0.014*\"after\" + 0.013*\"him\" + 0.011*\"who\" + 0.011*\"career\" + 0.010*\"had\" + 0.010*\"later\" + 0.009*\"where\"')], 'mean_ram': '856 MB', 'l2_norm': None, 'model': 'lda', 'max_ram': '857 MB', 'f1': None, 'train_time': Timedelta('0 days 01:19:07'), 'coherence': -1.7641}\n"
     ]
    }
   ],
   "source": [
    "lda = LdaModel.load('lda.model')\n",
    "row.update(get_metrics(lda, test_corpus))\n",
    "print(row)\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Sklearn NMF and record its metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Careful!** Sklearn loads the entire input Wikipedia matrix into RAM. Even though the matrix is sparse, **you'll need FIXME GB of free RAM to run the cell below**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_ram': '17940 MB', 'train_time': Timedelta('0 days 00:52:59'), 'model': 'sklearn_nmf', 'mean_ram': '12849 MB'}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['sklearn_nmf.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = {}\n",
    "row['model'] = 'sklearn_nmf'\n",
    "sklearn_nmf = SklearnNmf(n_components=50, tol=1e-2, random_state=42)\n",
    "row['train_time'], row['mean_ram'], row['max_ram'], sklearn_nmf = get_train_time_and_ram(\n",
    "    lambda: sklearn_nmf.fit(scipy.sparse.load_npz('wiki_train_csr.npz')),\n",
    "    'sklearn_nmf',\n",
    "    10,\n",
    ")\n",
    "print(row)\n",
    "joblib.dump(sklearn_nmf, 'sklearn_nmf.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-03-04 04:36:41,156 : INFO : CorpusAccumulator accumulated stats from 1000 documents\n",
      "2019-03-04 04:36:41,302 : INFO : CorpusAccumulator accumulated stats from 2000 documents\n",
      "2019-03-04 04:36:41,456 : INFO : CorpusAccumulator accumulated stats from 3000 documents\n",
      "2019-03-04 04:36:41,606 : INFO : CorpusAccumulator accumulated stats from 4000 documents\n",
      "2019-03-04 04:36:41,762 : INFO : CorpusAccumulator accumulated stats from 5000 documents\n",
      "2019-03-04 04:36:41,915 : INFO : CorpusAccumulator accumulated stats from 6000 documents\n",
      "2019-03-04 04:36:42,064 : INFO : CorpusAccumulator accumulated stats from 7000 documents\n",
      "2019-03-04 04:36:42,207 : INFO : CorpusAccumulator accumulated stats from 8000 documents\n",
      "2019-03-04 04:36:42,359 : INFO : CorpusAccumulator accumulated stats from 9000 documents\n",
      "2019-03-04 04:36:42,517 : INFO : CorpusAccumulator accumulated stats from 10000 documents\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'topics': [(0, '0.067*\"gmina\" + 0.067*\"poland\" + 0.066*\"voivodeship\" + 0.047*\"administrative\" + 0.043*\"lies\" + 0.038*\"approximately\" + 0.037*\"east\" + 0.032*\"west\" + 0.031*\"county\" + 0.03*\"regional\"'), (1, '0.098*\"district\" + 0.077*\"romanized\" + 0.075*\"iran\" + 0.071*\"rural\" + 0.062*\"census\" + 0.061*\"province\" + 0.054*\"families\" + 0.048*\"population\" + 0.043*\"county\" + 0.031*\"village\"'), (2, '0.095*\"apelor\" + 0.09*\"bucurești\" + 0.047*\"cadastrul\" + 0.047*\"hidrologie\" + 0.047*\"meteorologie\" + 0.047*\"institutul\" + 0.047*\"române\" + 0.047*\"româniei\" + 0.047*\"rîurile\" + 0.046*\"river\"'), (3, '0.097*\"commune\" + 0.05*\"department\" + 0.045*\"communes\" + 0.03*\"insee\" + 0.029*\"france\" + 0.018*\"population\" + 0.015*\"saint\" + 0.014*\"region\" + 0.01*\"town\" + 0.01*\"french\"'), (4, '0.148*\"township\" + 0.05*\"county\" + 0.018*\"townships\" + 0.018*\"unincorporated\" + 0.015*\"community\" + 0.011*\"indiana\" + 0.01*\"census\" + 0.01*\"creek\" + 0.009*\"pennsylvania\" + 0.009*\"illinois\"')], 'mean_ram': '12849 MB', 'l2_norm': 94.8459, 'model': 'sklearn_nmf', 'max_ram': '17940 MB', 'f1': None, 'train_time': Timedelta('0 days 00:52:59'), 'coherence': -2.0476}\n"
     ]
    }
   ],
   "source": [
    "sklearn_nmf = joblib.load('sklearn_nmf.joblib')\n",
    "row.update(get_metrics(\n",
    "    sklearn_nmf, test_corpus_tfidf, dictionary=dictionary,\n",
    "))\n",
    "print(row)\n",
    "tm_metrics = tm_metrics.append(pd.Series(row), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wikipedia results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>train_time</th>\n",
       "      <th>mean_ram</th>\n",
       "      <th>max_ram</th>\n",
       "      <th>coherence</th>\n",
       "      <th>l2_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gensim_nmf</td>\n",
       "      <td>00:27:09</td>\n",
       "      <td>794 MB</td>\n",
       "      <td>797 MB</td>\n",
       "      <td>-2.1426</td>\n",
       "      <td>94.9842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lda</td>\n",
       "      <td>01:19:07</td>\n",
       "      <td>856 MB</td>\n",
       "      <td>857 MB</td>\n",
       "      <td>-1.7641</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sklearn_nmf</td>\n",
       "      <td>00:52:59</td>\n",
       "      <td>12849 MB</td>\n",
       "      <td>17940 MB</td>\n",
       "      <td>-2.0476</td>\n",
       "      <td>94.8459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         model train_time  mean_ram   max_ram  coherence  l2_norm\n",
       "0   gensim_nmf   00:27:09    794 MB    797 MB    -2.1426  94.9842\n",
       "1          lda   01:19:07    856 MB    857 MB    -1.7641        -\n",
       "2  sklearn_nmf   00:52:59  12849 MB  17940 MB    -2.0476  94.8459"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm_metrics.replace(np.nan, '-', inplace=True)\n",
    "tm_metrics.drop(['topics', 'f1'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insights\n",
    "\n",
    "Gensim's online NMF outperforms Sklearn's NMF in terms of speed and RAM consumption:\n",
    "\n",
    "- **2x** faster.\n",
    "\n",
    "- Uses **~20x** less memory.\n",
    "\n",
    "    About **8GB** of Sklearn's RAM comes from the in-memory input matrices, which, in contrast to Gensim NMF, cannot be streamed iteratively. But even if we forget about the huge input size, Sklearn NMF uses about **2-8 GB** of RAM – significantly more than Gensim NMF or LDA.\n",
    "\n",
    "- L2 norm and coherence are a bit worse.\n",
    "\n",
    "Compared to Gensim's LDA, Gensim NMF also gives superior results:\n",
    "\n",
    "- **3x** faster\n",
    "- Coherence is worse than LDA's though."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learned Wikipedia topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gensim_nmf:\n",
      "(21, '0.009*\"his\" + 0.005*\"that\" + 0.005*\"him\" + 0.004*\"had\" + 0.003*\"they\" + 0.003*\"who\" + 0.003*\"her\" + 0.003*\"but\" + 0.003*\"king\" + 0.003*\"were\"')\n",
      "(39, '0.005*\"are\" + 0.005*\"or\" + 0.004*\"be\" + 0.004*\"that\" + 0.003*\"can\" + 0.003*\"used\" + 0.003*\"this\" + 0.002*\"have\" + 0.002*\"such\" + 0.002*\"which\"')\n",
      "(45, '0.091*\"apelor\" + 0.086*\"bucurești\" + 0.051*\"river\" + 0.046*\"cadastrul\" + 0.045*\"hidrologie\" + 0.045*\"meteorologie\" + 0.045*\"institutul\" + 0.045*\"române\" + 0.045*\"româniei\" + 0.045*\"rîurile\"')\n",
      "(28, '0.066*\"gmina\" + 0.065*\"poland\" + 0.065*\"voivodeship\" + 0.046*\"village\" + 0.045*\"administrative\" + 0.042*\"lies\" + 0.037*\"approximately\" + 0.036*\"east\" + 0.031*\"west\" + 0.030*\"county\"')\n",
      "(34, '0.087*\"romanized\" + 0.085*\"iran\" + 0.067*\"province\" + 0.067*\"rural\" + 0.066*\"census\" + 0.060*\"families\" + 0.054*\"village\" + 0.049*\"county\" + 0.047*\"population\" + 0.042*\"district\"')\n",
      "\n",
      "lda:\n",
      "(11, '0.066*\"de\" + 0.034*\"art\" + 0.030*\"french\" + 0.028*\"la\" + 0.022*\"france\" + 0.019*\"paris\" + 0.017*\"le\" + 0.016*\"museum\" + 0.013*\"van\" + 0.013*\"saint\"')\n",
      "(45, '0.033*\"new\" + 0.027*\"states\" + 0.025*\"united\" + 0.023*\"york\" + 0.023*\"american\" + 0.023*\"county\" + 0.021*\"state\" + 0.017*\"city\" + 0.014*\"california\" + 0.012*\"washington\"')\n",
      "(40, '0.028*\"radio\" + 0.025*\"show\" + 0.021*\"tv\" + 0.020*\"television\" + 0.016*\"news\" + 0.015*\"station\" + 0.014*\"channel\" + 0.012*\"fm\" + 0.012*\"network\" + 0.011*\"media\"')\n",
      "(28, '0.064*\"university\" + 0.018*\"research\" + 0.015*\"college\" + 0.014*\"institute\" + 0.013*\"science\" + 0.011*\"professor\" + 0.010*\"has\" + 0.010*\"international\" + 0.009*\"national\" + 0.009*\"society\"')\n",
      "(20, '0.179*\"he\" + 0.123*\"his\" + 0.015*\"born\" + 0.014*\"after\" + 0.013*\"him\" + 0.011*\"who\" + 0.011*\"career\" + 0.010*\"had\" + 0.010*\"later\" + 0.009*\"where\"')\n",
      "\n",
      "sklearn_nmf:\n",
      "(0, '0.067*\"gmina\" + 0.067*\"poland\" + 0.066*\"voivodeship\" + 0.047*\"administrative\" + 0.043*\"lies\" + 0.038*\"approximately\" + 0.037*\"east\" + 0.032*\"west\" + 0.031*\"county\" + 0.03*\"regional\"')\n",
      "(1, '0.098*\"district\" + 0.077*\"romanized\" + 0.075*\"iran\" + 0.071*\"rural\" + 0.062*\"census\" + 0.061*\"province\" + 0.054*\"families\" + 0.048*\"population\" + 0.043*\"county\" + 0.031*\"village\"')\n",
      "(2, '0.095*\"apelor\" + 0.09*\"bucurești\" + 0.047*\"cadastrul\" + 0.047*\"hidrologie\" + 0.047*\"meteorologie\" + 0.047*\"institutul\" + 0.047*\"române\" + 0.047*\"româniei\" + 0.047*\"rîurile\" + 0.046*\"river\"')\n",
      "(3, '0.097*\"commune\" + 0.05*\"department\" + 0.045*\"communes\" + 0.03*\"insee\" + 0.029*\"france\" + 0.018*\"population\" + 0.015*\"saint\" + 0.014*\"region\" + 0.01*\"town\" + 0.01*\"french\"')\n",
      "(4, '0.148*\"township\" + 0.05*\"county\" + 0.018*\"townships\" + 0.018*\"unincorporated\" + 0.015*\"community\" + 0.011*\"indiana\" + 0.01*\"census\" + 0.01*\"creek\" + 0.009*\"pennsylvania\" + 0.009*\"illinois\"')\n"
     ]
    }
   ],
   "source": [
    "def compare_topics(tm_metrics):\n",
    "    for _, row in tm_metrics.iterrows():\n",
    "        print('\\n{}:'.format(row.model))\n",
    "        print(\"\\n\".join(str(topic) for topic in row.topics))\n",
    "        \n",
    "compare_topics(tm_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems all three models successfully learned useful topics from the Wikipedia corpus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. And now for something completely different: Face decomposition from images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NMF algorithm in Gensim is optimized for extremely large (sparse) text corpora, but it will also work on vectors from other domains!\n",
    "\n",
    "Let's compare our model to other factorization algorithms on dense image vectors and check out the results.\n",
    "\n",
    "To do that we'll patch sklearn's [Faces Dataset Decomposition](https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sklearn wrapper\n",
    "Let's create an Scikit-learn wrapper in order to run Gensim NMF on images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from numpy.random import RandomState\n",
    "from sklearn import decomposition\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.datasets import fetch_olivetti_faces\n",
    "from sklearn.decomposition.nmf import NMF as SklearnNmf\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "\n",
    "import gensim.downloader\n",
    "from gensim import matutils\n",
    "from gensim.corpora import Dictionary\n",
    "from gensim.models import CoherenceModel, LdaModel, LdaMulticore\n",
    "from gensim.models.nmf import Nmf as GensimNmf\n",
    "from gensim.parsing.preprocessing import preprocess_string\n",
    "\n",
    "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "import scipy.sparse as sparse\n",
    "\n",
    "\n",
    "class NmfWrapper(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, bow_matrix, **kwargs):\n",
    "        self.corpus = sparse.csc.csc_matrix(bow_matrix)\n",
    "        self.nmf = GensimNmf(**kwargs)\n",
    "\n",
    "    def fit(self, X):\n",
    "        self.nmf.update(self.corpus)\n",
    "\n",
    "    @property\n",
    "    def components_(self):\n",
    "        return self.nmf.get_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modified face decomposition notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted from the excellent [Scikit-learn tutorial](https://github.com/scikit-learn/scikit-learn/blob/master/examples/decomposition/plot_faces_decomposition.py) (BSD license):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turn off the logger due to large number of info messages during training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "gensim.models.nmf.logger.propagate = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================\n",
      "Faces dataset decompositions\n",
      "============================\n",
      "\n",
      "This example applies to :ref:`olivetti_faces` different unsupervised\n",
      "matrix decomposition (dimension reduction) methods from the module\n",
      ":py:mod:`sklearn.decomposition` (see the documentation chapter\n",
      ":ref:`decompositions`) .\n",
      "\n",
      "\n",
      "Dataset consists of 400 faces\n",
      "Extracting the top 6 Eigenfaces - PCA using randomized SVD...\n",
      "done in 0.024s\n",
      "Extracting the top 6 Non-negative components - NMF (Sklearn)...\n",
      "done in 0.164s\n",
      "Extracting the top 6 Non-negative components - NMF (Gensim)...\n",
      "done in 0.544s\n",
      "Extracting the top 6 Independent components - FastICA...\n",
      "done in 0.090s\n",
      "Extracting the top 6 Sparse comp. - MiniBatchSparsePCA...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anotherbugmaster/.local/lib/python3.5/site-packages/sklearn/decomposition/sparse_pca.py:405: DeprecationWarning: normalize_components=False is a backward-compatible setting that implements a non-standard definition of sparse PCA. This compatibility mode will be removed in 0.22.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done in 1.536s\n",
      "Extracting the top 6 MiniBatchDictionaryLearning...\n",
      "done in 0.461s\n",
      "Extracting the top 6 Cluster centers - MiniBatchKMeans...\n",
      "done in 0.064s\n",
      "Extracting the top 6 Factor Analysis components - FA...\n",
      "done in 0.046s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/anotherbugmaster/.local/lib/python3.5/site-packages/sklearn/decomposition/factor_analysis.py:238: ConvergenceWarning: FactorAnalysis did not converge. You might want to increase the number of iterations.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x162.72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x325.44 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "============================\n",
    "Faces dataset decompositions\n",
    "============================\n",
    "\n",
    "This example applies to :ref:`olivetti_faces` different unsupervised\n",
    "matrix decomposition (dimension reduction) methods from the module\n",
    ":py:mod:`sklearn.decomposition` (see the documentation chapter\n",
    ":ref:`decompositions`) .\n",
    "\n",
    "\"\"\"\n",
    "print(__doc__)\n",
    "\n",
    "# Authors: Vlad Niculae, Alexandre Gramfort\n",
    "# License: BSD 3 claus\n",
    "\n",
    "n_row, n_col = 2, 3\n",
    "n_components = n_row * n_col\n",
    "image_shape = (64, 64)\n",
    "rng = RandomState(0)\n",
    "\n",
    "# #############################################################################\n",
    "# Load faces data\n",
    "dataset = fetch_olivetti_faces(shuffle=True, random_state=rng)\n",
    "faces = dataset.data\n",
    "\n",
    "n_samples, n_features = faces.shape\n",
    "\n",
    "# global centering\n",
    "faces_centered = faces - faces.mean(axis=0)\n",
    "\n",
    "# local centering\n",
    "faces_centered -= faces_centered.mean(axis=1).reshape(n_samples, -1)\n",
    "\n",
    "print(\"Dataset consists of %d faces\" % n_samples)\n",
    "\n",
    "\n",
    "def plot_gallery(title, images, n_col=n_col, n_row=n_row):\n",
    "    plt.figure(figsize=(2. * n_col, 2.26 * n_row))\n",
    "    plt.suptitle(title, size=16)\n",
    "    for i, comp in enumerate(images):\n",
    "        plt.subplot(n_row, n_col, i + 1)\n",
    "        vmax = max(comp.max(), -comp.min())\n",
    "        plt.imshow(comp.reshape(image_shape), cmap=plt.cm.gray,\n",
    "                   interpolation='nearest',\n",
    "                   vmin=-vmax, vmax=vmax)\n",
    "        plt.xticks(())\n",
    "        plt.yticks(())\n",
    "    plt.subplots_adjust(0.01, 0.05, 0.99, 0.93, 0.04, 0.)\n",
    "\n",
    "\n",
    "# #############################################################################\n",
    "# List of the different estimators, whether to center and transpose the\n",
    "# problem, and whether the transformer uses the clustering API.\n",
    "estimators = [\n",
    "    ('Eigenfaces - PCA using randomized SVD',\n",
    "     decomposition.PCA(n_components=n_components, svd_solver='randomized',\n",
    "                       whiten=True),\n",
    "     True),\n",
    "\n",
    "    ('Non-negative components - NMF (Sklearn)',\n",
    "     decomposition.NMF(n_components=n_components, init='nndsvda', tol=5e-3),\n",
    "     False),\n",
    "\n",
    "    ('Non-negative components - NMF (Gensim)',\n",
    "     NmfWrapper(\n",
    "         bow_matrix=faces.T,\n",
    "         chunksize=3,\n",
    "         eval_every=400,\n",
    "         passes=2,\n",
    "         id2word={idx: idx for idx in range(faces.shape[1])},\n",
    "         num_topics=n_components,\n",
    "         minimum_probability=0,\n",
    "         random_state=42,\n",
    "     ),\n",
    "     False),\n",
    "\n",
    "    ('Independent components - FastICA',\n",
    "     decomposition.FastICA(n_components=n_components, whiten=True),\n",
    "     True),\n",
    "\n",
    "    ('Sparse comp. - MiniBatchSparsePCA',\n",
    "     decomposition.MiniBatchSparsePCA(n_components=n_components, alpha=0.8,\n",
    "                                      n_iter=100, batch_size=3,\n",
    "                                      random_state=rng),\n",
    "     True),\n",
    "\n",
    "    ('MiniBatchDictionaryLearning',\n",
    "     decomposition.MiniBatchDictionaryLearning(n_components=15, alpha=0.1,\n",
    "                                               n_iter=50, batch_size=3,\n",
    "                                               random_state=rng),\n",
    "     True),\n",
    "\n",
    "    ('Cluster centers - MiniBatchKMeans',\n",
    "     MiniBatchKMeans(n_clusters=n_components, tol=1e-3, batch_size=20,\n",
    "                     max_iter=50, random_state=rng),\n",
    "     True),\n",
    "\n",
    "    ('Factor Analysis components - FA',\n",
    "     decomposition.FactorAnalysis(n_components=n_components, max_iter=2),\n",
    "     True),\n",
    "]\n",
    "\n",
    "# #############################################################################\n",
    "# Plot a sample of the input data\n",
    "\n",
    "plot_gallery(\"First centered Olivetti faces\", faces_centered[:n_components])\n",
    "\n",
    "# #############################################################################\n",
    "# Do the estimation and plot it\n",
    "\n",
    "for name, estimator, center in estimators:\n",
    "    print(\"Extracting the top %d %s...\" % (n_components, name))\n",
    "    t0 = time.time()\n",
    "    data = faces\n",
    "    if center:\n",
    "        data = faces_centered\n",
    "    estimator.fit(data)\n",
    "    train_time = (time.time() - t0)\n",
    "    print(\"done in %0.3fs\" % train_time)\n",
    "    if hasattr(estimator, 'cluster_centers_'):\n",
    "        components_ = estimator.cluster_centers_\n",
    "    else:\n",
    "        components_ = estimator.components_\n",
    "\n",
    "    # Plot an image representing the pixelwise variance provided by the\n",
    "    # estimator e.g its noise_variance_ attribute. The Eigenfaces estimator,\n",
    "    # via the PCA decomposition, also provides a scalar noise_variance_\n",
    "    # (the mean of pixelwise variance) that cannot be displayed as an image\n",
    "    # so we skip it.\n",
    "    if (hasattr(estimator, 'noise_variance_') and\n",
    "            estimator.noise_variance_.ndim > 0):  # Skip the Eigenfaces case\n",
    "        plot_gallery(\"Pixelwise variance\",\n",
    "                     estimator.noise_variance_.reshape(1, -1), n_col=1,\n",
    "                     n_row=1)\n",
    "    plot_gallery('%s - Train time %.1fs' % (name, train_time),\n",
    "                 components_[:n_components])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, Gensim's NMF implementation is slower than Sklearn's on **dense** vectors, while achieving comparable quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "Gensim NMF is an extremely fast and memory-optimized model. Use it to obtain interpretable topics, as an alternative to SVD / LDA.\n",
    "\n",
    "---\n",
    "\n",
    "The NMF implementation in Gensim was created by [Timofey Yefimov](https://github.com/anotherbugmaster/) as a part of his [RARE Technologies Student Incubator](https://rare-technologies.com/incubator/) graduation project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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